The Acute Respiratory Distress Syndrome (ARDS) is common, costly, and responsible for high mortality and long-term morbidity. An estimated 200,000 Americans develop ARDS each year, of which more than 74,000 die from the disease. There is a significant unmet clinical need both for early rapid detection and diagnosis as well as clinical trajectory monitoring of ARDS. Current methods of detection include various scoring systems that have unacceptably low positive predictive values, and tools such as chest radiographs and gas exchange metrics, which temporally lag the acute, dynamic inflammatory processes responsible for ARDS. This prevents early recognition and treatment of ARDS, resulting in increased mortality. Exhaled breath contains volatile organic (VOCs) that could potentially be used noninvasively to predict the onset and severity of various diseases and to guide therapy. Real-time monitoring of VOCs could produce point-of-care (POC) breathomic signatures capable of rapidly detecting lung and systemic disease processes such as ARDS and sepsis. Such POC monitoring of lung and systemic metabolomics could prove transformative in managing the dynamic physiology in critical care and emergency medicine. Unfortunately, no vapor analyzer exists to detect these exhaled biomarkers with the part-per-billion (ppb) sensitivity and rapid response time necessary to allow for POC testing. This project proposes to develop a portable, fully automated, high-performance multi-dimensional micro-gas chromatography (GC) device that can be attached to a mechanical ventilator and is capable of rapidly and continuously detecting exhaled VOCs specific to ARDS. Our objectives are to identify breathomic patterns for the early detection, stratification, and trajectory monitoring of ARDS. In this project, we propose 2 specific aims: Aim 1: Refine the GC device and expand VOC targets. We will refine and evolve our current 1x4-channel 2- dimensional (2-D) GC device to a 1x2x4-channel 3-D device capable of detecting and quantifying additional important inflammation related VOCs. A 3-D VOC reference library will be created for in-situ analyte identification and quantitation. Aim 2: Identify exhaled breath biomarkers related to the onset, severity, and resolution of ARDS. Under IRB approval, we will use the portable GC to study mechanically ventilated patients with and without ARDS in a time series manner to collect GC, physiologic, and demographic data allowing for detection and analysis of breathomic signatures associated with the onset, severity, and trajectory of ARDS. The potential to identify breathomic patterns used for early diagnosis, disease trajectory tracking, and outcome prediction monitoring of ARDS would have significant impact on changing practice and improving patient outcomes. The same device and methods developed in the proposed project can also be extended to other diseases such as chronic obstructive pulmonary disease and pneumonia, and to other clinical settings outside of the intensive care unit.